RESUMO
In the 21st century, groundwater depletion is posing a serious threat to humanity throughout the world, particularly in developing nations. India being the largest consumer of groundwater in the world, dwindling groundwater storage has emerged as a serious concern in recent years. Consequently, the judicious and efficient management of vital groundwater resources is one of the grand challenges in India. Groundwater modeling is a promising tool to develop sustainable management strategies for the efficient utilization of this treasured resource. This study demonstrates a pragmatic framework for predicting seasonal groundwater levels at a large scale using real-world data. Three relatively powerful Machine Learning (ML) techniques viz., ANFIS (Adaptive Neuro-Fuzzy Inference System), Deep Neural Network (DNN) and Support Vector Machine (SVM) were employed for predicting seasonal groundwater levels at the country scale using in situ groundwater-level and pertinent meteorological data of 1996-2016. ANFIS, DNN and SVM models were developed for 18 Agro-Ecological Zones (AEZs) of India and their efficacy was evaluated using suitable statistical and graphical indicators. The findings of this study revealed that the DNN model is the most proficient in predicting seasonal groundwater levels in most AEZs, followed by the ANFIS model. However, the prediction ability of the three models is 'moderate' to 'very poor' in 3 AEZs ['Western Plain and Kutch Peninsula' in Western India, and 'Deccan Plateau (Arid)' and 'Eastern Ghats and Deccan Plateau' in Southern India]. It is recommended that groundwater-monitoring network and data acquisition systems be strengthened in India in order to ensure efficient use of modeling techniques for the sustainable management of groundwater resources.
RESUMO
Background: Abiotic stresses affect plants in several ways and as such, phytohormones such as abscisic acid (ABA) play an important role in conferring tolerance towards these stresses. Hence, to comprehend the role of ABA and its interaction with receptors of the plants, a thorough investigation is essential. Aim: The current study aimed to identify the ABA receptors in Oryza sativa, to find the receptor that binds best with ABA and to examine the mutations present to help predict better binding of the receptors with ABA. Methods: Protein sequences of twelve PYL (Pyrabactin resistance 1) and seven PP2C (type 2C protein phosphatase) receptors were retrieved from the Rice Annotation Project database and their 3D structures were predicted using RaptorX. Protein-ligand molecular docking studies between PYL and ABA were performed using AutoDock 1.5.6, followed by 100ns molecular dynamic simulation studies using Desmond to determine the acceptable conformational changes after docking via root mean square deviation RMSD plot analysis. Protein-protein docking was then carried out in three sets: PYL-PP2Cs, PYL-ABA-PP2C and PYL(mut)-ABA-PP2C to scrutinize changes in structural conformations and binding energies between complexes. The amino acids of interest were mapped at their respective genomic coordinates using SNP-seek database to ascertain if there were any naturally occurring single nucleotide polymorphisms (SNPs) responsible for triggering rice PYLs mutations. Results: Initial protein-ligand docking studies revealed good binding between the complexes, wherein PYL6-ABA complex showed the best energy of -8.15 kcal/mol. The 100ns simulation studies revealed changes in the RMSD values after docking, indicating acceptable conformational changes. Furthermore, mutagenesis study performed at specific PYL-ABA interacting residues followed by downstream PYL(mut)-ABA-PP2C protein-protein docking results after induction of mutations demonstrated binding energy of -8.17 kcal/mol for PP2C79-PYL11-ABA complex. No naturally occurring SNPs that were responsible for triggering rice PYL mutations were identified when specific amino acid coordinates were mapped at respective genomic coordinates. Conclusion: Thus, the present study provides valuable insights on the interactions of ABA receptors in rice and induced mutations in PYL11 that can enhance the downstream interaction with PP2C.
RESUMO
Although gum kondagogu (Cochlospermum gossypium) is grouped under gum karaya (Sterculia sp.), it differs significantly in terms of physicochemical properties and chemical composition and does not conform to the confirmatory tests prescribed for gum karaya ([Janaki]). Gum karaya has wide applications in the pharmaceutical and food industries, whereas the use of gum kondagogu is yet to be explored. In this context, a short-term toxicity study on gum kondagogu was undertaken in rats. The gum was fed to rats at 0, 0.2%, 1% and 5% (w/w) in feed, for 90 days. Biochemical parameters were measured to assess the toxicity at the end of the study period. The results indicated no significant changes in growth pattern, haematological indices (RBC, WBC, Hb, PCV, MCV, MCH, MCHC, differential leucocyte counts), biochemical analytes (glucose, urea nitrogen, total protein, albumin, bilirubin, creatinine, sodium and potassium ions), activities of plasma and liver enzymes (alkaline phosphatase, alanine amino-transaminase, aspartate aminotransaminase, lactate dehydrogenase, glutathione S-transferase and gamma-glutamyl transpeptidases and organ to body mass ratio (brain, heart, lungs, liver, kidneys and spleen). Histopathology of the liver and kidney also did not reveal any abnormality. An increased faecal bulk was observed in rats fed with 5% gum kondagogu. However, faecal moisture content of female rats only was significantly different (P=<0.05) as compared to controls. Thus, it can be inferred, based on the present investigations, that gum kondagogu has a potential application as a food additive, similar to gum karaya. Feeding it at a much higher level (5%) than expected for consumption as a food additive also did not result in any toxic effect. Being non-toxic, gum kondagogu has a potential as a food additive with excellent physicochemical properties and a unique chemical composition.
